Automatic breast ultrasound image segmentation: a survey

Xian, Min; Zhang, Yingtao; Cheng, H.D.; Xu, Fei; Zhang, Boyu; Ding, Jianrui

doi:10.1016/j.patcog.2018.02.012

Cited by 284Medical Image Segmentation TechniquesAI in cancer detectionBrain Tumor Detection and Classification

Automatic breast ultrasound image segmentation: a survey

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Xian, Min, Zhang, Yingtao, Cheng, H.D., Xu, Fei, Zhang, Boyu, Ding, Jianrui·2018·Pattern Recognition

DOI 10.1016/j.patcog.2018.02.012

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Few review articles focus on medical US analysis, aside from one or two papers examining specific tasks such as breast US image segmentation.
“However, few focus on medical US analysis, aside from one or two papers that examine specific tasks such as breast US image segmentation [55].”
From Deep Learning in Medical Ultrasound Analysis: A Review (2019) · cites this paper as [55]

Support-vector networks
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Background
Applied logistic regression
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Support-Vector Networks
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Pattern Recognition with Fuzzy Objective Function Algorithms
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MethodInfluential
Cancer statistics, 2015
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Active contours without edges
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MethodInfluential
Object Detection with Discriminatively Trained Part-Based Models
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Method
SLIC Superpixels Compared to State-of-the-Art Superpixel Methods
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Pattern Recognition and Machine Learning
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“, mean filter, Gaussian low-pass filter, speckle reducing anisotropic diffusion (SRAD) [148], nonlinear coherence diffusion (NCD) [149], sticks filter [60], bilateral filter [150, 151], fractional subpixel diffusion [157], etc.”
Background
Fast approximate energy minimization via graph cuts
Boykov, Y., Veksler, O., & Zabih, R. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.969114 · Cited by 6,988 · see citing works
A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters
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Geodesic Active Contours
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Deep learning in medical image analysis
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An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision
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Mean shift, mode seeking, and clustering
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Multi-column deep neural networks for image classification
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“Learning-based approaches thrive in BUS image segmentation in the last decade and we believe new deep learning techniques [162] such as deep convolutional neural networks (CNNs) and recurrent neural network (RNN) will make great progress in segmenting BUS images in the near future.”
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Shape modeling with front propagation: a level set approach
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What energy functions can be minimized via graph cuts?
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Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials
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Random Walks for Image Segmentation
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“Second, random walk [173] was applied to generate the final tumor boundary by placing seed at the center of each true region.”
Method
Calculation of pressure fields from arbitrarily shaped, apodized, and excited ultrasound transducers
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Maximal Flow Through a Network
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On active contour models and balloons
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BackgroundMethod
Distance Regularized Level Set Evolution and Its Application to Image Segmentation
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Maximal Flow Through a Network
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“The approach utilized user interaction to generate the volume of interest (VOI), applied the radial-gradient index [83] to estimate the initial lesion boundary, and implemented the region-based DM [48, 49] iteratively to find the final contour.”
Method
Deep Learning: Methods and Applications
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A geometric model for active contours in image processing
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BackgroundInfluential
Minimization of Region-Scalable Fitting Energy for Image Segmentation
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“The approach utilized user interaction to generate the volume of interest (VOI), applied the radial-gradient index [83] to estimate the initial lesion boundary, and implemented the region-based DM [48, 49] iteratively to find the final contour.”
Method
Support vector machine learning for interdependent and structured output spaces
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“In [28], both the region-level [29] and pixellevel features were utilized to define the weights in the data term; and all weights were learned by using a structural SVM [30].”
BackgroundMethodInfluential
Breast cancer in China.
Fan, L., Strasser-Weippl, K., Li, J.-J., Louis, J. S., Finkelstein, D., Yu, K.-D., Chen, W.-Q., Shao, Z., & Goss, P. (2014) · The Lancet Oncology · doi:10.1016/s1470-2045(13)70567-9 · Cited by 1,391 · cited 1× · see citing works
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Background
Finite-element methods for active contour models and balloons for 2-D and 3-D images
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“[145, 146] applied RGI to find the initial tumor contour of 3D BUS image.”
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Fuzzy c-means clustering with spatial information for image segmentation
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“[87] proposed a BUS image segmentation method using the spatial FCM (sFCM) [88] with local texture and intensity features.”
Method
New types of deep neural network learning for speech recognition and related applications: an overview
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“Deep learning: in the last several years, deep learning has demonstrated impressive performance for many tasks such as object recognition [158], image classification [159], semantic segmentation [160], facial expression recognition [163], speech recognition [164], medical applications [161, 170], etc.”
Background
TurboPixels: Fast Superpixels Using Geometric Flows
Levinshtein, A., Stere, A., Kutulakos, K. N., Fleet, D. J., Dickinson, S., & Kaleem Siddiqi, A. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.96 · Cited by 1,146 · cited 1× · see citing works
“For state-of-the-art superpixel generation approaches, refer [177-179].”
Localizing Region-Based Active Contours
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“25 / 71 For the points in the low contrast class, both the global and local region-based energies [66, 77] are used; while for the high contrast class, only the local region-based [77] energy is utilized.”
Method
Exact Maximum A Posteriori Estimation for Binary Images
Greig, D. M., Porteous, B. T., & Seheult, A. H. (1989) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.2517-6161.1989.tb01764.x · Cited by 1,066 · cited 1× · see citing works
“2 Graph cuts Graph cuts was proposed to solve a special case in MRF-MAP framework [16]: the global optimization of the binary labelling problem (L = { , }).”
Method
Multi-column deep neural network for traffic sign classification
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Geodesic active contours and level sets for the detection and tracking of moving objects
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“The approach was compared with two other GDM approaches [66, 68] using 79 BUS images.”
Method
Use of watersheds in contour detection
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Level set methods and dynamic implicit surfaces
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“For more information about the reinitialization, please refer [45-47].”
Background
Large scale deep learning for computer aided detection of mammographic lesions
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The Morphological Approach to Segmentation: The Watershed Transformation
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“The most common implementation of watershed for image segmentation can be found in [121].”
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On the origin of the bilateral filter and ways to improve it
Elad, M. (2002) · IEEE Transactions on Image Processing · doi:10.1109/tip.2002.801126 · Cited by 795 · cited 1× · see citing works
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Background
Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans
Cheng, J.-Z., Ni, D., Chou, Y.-H., Qin, J., Tiu, C.-M., Chang, Y.-C., Huang, C.-S., Shen, D., & Chen, C.-M. (2016) · Scientific Reports · doi:10.1038/srep24454 · Cited by 755 · cited 1× · see citing works
“Deep learning: in the last several years, deep learning has demonstrated impressive performance for many tasks such as object recognition [158], image classification [159], semantic segmentation [160], facial expression recognition [163], speech recognition [164], medical applications [161, 170], etc.”
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Automated breast cancer detection and classification using ultrasound images: A survey
Cheng, H.-D., Shan, J., Ju, W., Guo, Y., & Zhang, L. (2009) · Pattern Recognition · doi:10.1016/j.patcog.2009.05.012 · Cited by 729 · cited 1× · see citing works
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Generalized gradient vector flow external forces for active contours
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“Gao et al. [73] proposed a level set approach for BUS image segmentation based on the method in [47] by redefining the edge-based stop function using phase congruency [74] which is invariant to intensity magnitude, and integrated GVF model into the level set framework.”
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Book Review: Pattern Recognition and Machine Learning
Nasrabadi, N. M. (2007) · Journal of Electronic Imaging · doi:10.1117/1.2819119 · Cited by 636 · cited 1× · see citing works
“Graph-based approaches Graph-based approaches gain increasing popularity in BUS image segmentation because they offer several advantages: (1) they provide a simple way to organize task-related priors and image information in a unified framework; (2) they are flexible and suitable for expressing soft constraints betwee…”
Background
Nonlocal means-based speckle filtering for ultrasound images
Pierrick Coupé, A., Pierre Hellier, J., Charles Kervrann, A., & Barillot, C. (2009) · IEEE Transactions on Image Processing · doi:10.1109/tip.2009.2024064 · Cited by 617 · cited 2× · see citing works
“, mean filter, Gaussian low-pass filter, speckle reducing anisotropic diffusion (SRAD) [148], nonlinear coherence diffusion (NCD) [149], sticks filter [60], bilateral filter [150, 151], fractional subpixel diffusion [157], nonlocal means-based speckle filter [167], etc.”
Background
A Tour of Modern Image Filtering: New Insights and Methods, Both Practical and Theoretical
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An adaptive clustering algorithm for image segmentation
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A geometric snake model for segmentation of medical imagery
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“In edge-based DMs, [42, 50, 52, 56 - 59, 61 - 63], image gradient is applied to constructing the external force or speed function of the evolving curve; as discussed in section 3.”
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A maximum-flow formulation of the N-camera stereo correspondence problem
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“It was then improved to solve the general (color image segmentation) and multi-label problem [17, 18].”
Background
Conformal curvature flows: From phase transitions to active vision
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“Edge-based DMs aim to attract the evolving curve to object boundary by defining the deformation force or speed using image gradient [42-44], and the models depend on image gradient to stop curve deformation.”
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A discrete dynamic contour model
Lobregt, S., & Viergever, M. A. (1995) · IEEE Transactions on Medical Imaging · doi:10.1109/42.370398 · Cited by 426 · cited 1× · see citing works
“[71] considered the SNR and local intensity value as two important features for estimating tumor boundary, and built a two-fold termination criterion based on the two features in discrete dynamic DMs [72].”
Method
Real-time speckle reduction and coherence enhancement in ultrasound imaging via nonlinear anisotropic diffusion
Abd‐Elmoniem, K. Z., Youssef, A. M., & Kadah, Y. M. (2002) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2002.1028423 · Cited by 404 · see citing works
Probabilistic boosting-tree: learning discriminative models for classification, recognition, and clustering
Tu, Z. (2005) · Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 · doi:10.1109/iccv.2005.194 · Cited by 397 · cited 3× · see citing works
“In [142], the contour with max ARD value [25,26] was chosen as the final tumor contour.”
Method
Phase congruency: A low-level image invariant
Kovesi, P. D. (2000) · Psychological Research · doi:10.1007/s004260000024 · Cited by 389 · cited 1× · see citing works
“[73] proposed a level set approach for BUS image segmentation based on the method in [47] by redefining the edge-based stop function using phase congruency [74] which is invariant to intensity magnitude, and integrated GVF model into the level set framework.”
Method
A Comparative Study of Energy Minimization Methods for Markov Random Fields
Szeliski, R., Ramin Zabih, A., Scharstein, D., Veksler, O., Kolmogorov, V., Agarwala, A., Tappen, M. F., & Rother, C. (2006) · Lecture notes in computer science · doi:10.1007/11744047_2 · Cited by 388 · cited 2× · see citing works
“A detailed comparison between ICM and other MRF energy minimization techniques can be found in [35].”
Background
Watershed Cuts: Minimum Spanning Forests and the Drop of Water Principle
Cousty, J., Bertrand, G., Najman, L. A., & Couprie, M. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.173 · Cited by 349 · cited 2× · see citing works
“, in [120], a well-trained ANN was applied to perform the tumor segmentation.”
Method
Combining low-, high-level and empirical domain knowledge for automated segmentation of ultrasonic breast lesions
Madabhushi, A., & Metaxas, D. (2003) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2002.808364 · Cited by 292 · see citing works
A Seeded Image Segmentation Framework Unifying Graph Cuts And Random Walker Which Yields A New Algorithm
Sinop, A. K., & Grady, L. J. (2007) · IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2007.4408927 · Cited by 279 · cited 1× · see citing works
“In graph-based models, it corresponds the smoothness term (pairwise energy), and the minimization of the energy makes the models produce a smooth boundary; however, it is important to notice that the smoothness term also makes the models have the tendency to shrink and generate much shorter boundary than the real boun…”
Background
Automated seeded lesion segmentation on digital mammograms
Kupinski, M. A., & Giger, M. L. (1998) · IEEE Transactions on Medical Imaging · doi:10.1109/42.730396 · Cited by 260 · cited 1× · see citing works
“(41)) which was proposed in [141] to segment mammograms.”
Background
Computer-Aided Diagnosis of Solid Breast Nodules: Use of an Artificial Neural Network Based on Multiple Sonographic Features
Joo, S., Yang, Y., Moon, W. K., & Kim, H. C. (2004) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2004.834617 · Cited by 255 · cited 2× · see citing works
“The first is to choose empirical value as the threshold for the whole dataset [109-111]; the second approach is to select the threshold for each image based on domain related rules [112, 113]; and the third is to generate the threshold automatically based on statistical-decision theory [22, 114, 115].”
Method
Computerized lesion detection on breast ultrasound
Drukker, K., Giger, M. L., Horsch, K., Kupinski, M. A., Vyborny, C. J., & Mendelson, E. B. (2002) · Medical Physics · doi:10.1118/1.1485995 · Cited by 234 · cited 2× · see citing works
“In [142], the contour with max ARD value [25,26] was chosen as the final tumor contour.”
Method
Breast ultrasound image segmentation: a survey
Huang, Q., Luo, Y., & Zhang, Q. (2017) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-016-1513-1 · Cited by 222 · cited 1× · see citing works
“[175] is the first survey paper for BUS image segmentation published recently; however, this paper is much more comprehensive in terms of major issue discussions, future direction prediction, theory fundamentals, application theme and the number of references.”
Background
Computerized diagnosis of breast lesions on ultrasound
Horsch, K., Giger, M. L., Venta, L. A., & Vyborny, C. J. (2002) · Medical Physics · doi:10.1118/1.1429239 · Cited by 207 · cited 4× · see citing works
“Cellular automata (CA): CA was introduced by von Neumann [133] and applied to interactive image segmentation [134].”
MethodInfluential
Watershed segmentation for breast tumor in 2-D sonography
Huang, Y.-L., & Chen, D. (2004) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2003.12.001 · Cited by 200 · cited 2× · see citing works
“[122] applied the watershed to segment the preprocessed BUS images and the markers were selected based on grey level and connectivity.”
Method
A robust snake implementation; a dual active contour
Gunn, S. R., & Nixon, M. S. (1997) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.566812 · Cited by 199 · cited 1× · see citing works
“22 / 71 dual snake [53] to handle the two problems of applying traditional DMs for BUS image segmentation: (1) initial contour should be placed close to the tumor boundary; and (2) cannot capture highly winding tumor boundary.”
Background
Automatic ultrasound segmentation and morphology based diagnosis of solid breast tumors
Chang, R., Wu, W.-J., Moon, W. K., & Chen, D. (2005) · Breast Cancer Research and Treatment · doi:10.1007/s10549-004-2043-z · Cited by 198 · see citing works
Segmentation of ultrasound B-mode images with intensity inhomogeneity correction
Xiao, G., Brady, M., Noble, J. A., & Zhang, Y. (2002) · IEEE Transactions on Medical Imaging · doi:10.1109/42.981233 · Cited by 188 · cited 5× · see citing works
“In order to improve the process of manual selection of Gaussian parameters in [11], reference [12] proposed a one-click user interaction to estimate Gaussian parameters automatically.”
BackgroundMethodInfluentialSelf-citation
Automatic segmentation of breast lesions on ultrasound
Horsch, K., Giger, M. L., Venta, L. A., & Vyborny, C. J. (2001) · Medical Physics · doi:10.1118/1.1386426 · Cited by 180 · cited 1× · see citing works
“[124] Watershed Pre-segmentation Decided the external and internal markers by computing the Beucher gradient [131] of the morphological dilation and erosion of the binary image Depended on image preprocessing; select 255 groups of markers by thresholding image using thresholds from 0 to 255; needed additional geometri…”
Method
Completely Automated Segmentation Approach for Breast Ultrasound Images Using Multiple-Domain Features
Shan, J., Cheng, H.-D., & Wang, Y. (2012) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2011.10.022 · Cited by 159 · cited 6× · see citing works
“Mean filter and Gaussian low-pass filter are simple and fast, and widely used in early BUS segmentation approaches [22, 52, 61, 59, 62, 63, 109, 115, 112, 123, 133, 138]; SRAD, NCD, sticks filter, and fractional subpixel diffusion are specially designed to deal with speckle noise, and because of their excellent proper…”
BackgroundMethodInfluentialSelf-citation
Fully automatic and segmentation-robust classification of breast tumors based on local texture analysis of ultrasound images
Liu, B., Cheng, H.-D., Huang, J., Tian, J., Tang, X., & Liu, J. (2009) · Pattern Recognition · doi:10.1016/j.patcog.2009.06.002 · Cited by 156 · cited 7× · see citing works
“Texture and other local region features have more descriptive power than intensity, and have been studied in many works [9, 10, 64, 98] that they can distinguish tumor regions from normal tissues with high accuracy.”
BackgroundMethodInfluentialSelf-citation
Detection of lines and boundaries in speckle images-application to medical ultrasound
Czerwinski, R. N., Jones, D. L., & O’Brien, W. D. (1999) · IEEE Transactions on Medical Imaging · doi:10.1109/42.759114 · Cited by 152 · cited 4× · see citing works
“[62] and some early works [58-61] detected tumor automatically based on low-level image features, e.”
BackgroundMethodInfluential
Probability density difference-based active contour for ultrasound image segmentation
Liu, B., Cheng, H.-D., Huang, J., Tian, J., Tang, X., & Liu, J. (2010) · Pattern Recognition · doi:10.1016/j.patcog.2010.01.002 · Cited by 148 · cited 2× · see citing works
“[67] model the probability density difference between the intensity distribution of tumor/background region and estimated Rayleigh distribution to improve the performance of GDM.”
BackgroundMethodSelf-citation
Wall position and thickness estimation from sequences of echocardiographic images
Bioucas‐Dias, J. M., & Leitão, J. M. N. (1996) · IEEE Transactions on Medical Imaging · doi:10.1109/42.481438 · Cited by 142 · cited 1× · see citing works
“46 / 71 In ultrasound imaging, speckle noise is inherent to coherent illumination and Rayleigh scattering caused by tissue microstructures [147], and it is a major difficulty in BUS image segmentation.”
Background
Fully automatic segmentation of breast ultrasound images based on breast characteristics in space and frequency domains
Xian, M., Zhang, Y., & Cheng, H.-D. (2014) · Pattern Recognition · doi:10.1016/j.patcog.2014.07.026 · Cited by 141 · cited 10× · see citing works
“Mean filter and Gaussian low-pass filter are simple and fast, and widely used in early BUS segmentation approaches [22, 52, 61, 59, 62, 63, 109, 115, 112, 123, 133, 138]; SRAD, NCD, sticks filter, and fractional subpixel diffusion are specially designed to deal with speckle noise, and because of their excellent proper…”
BackgroundMethodInfluentialSelf-citation
Segmentation of ultrasound images––multiresolution 2D and 3D algorithm based on global and local statistics
Djamal Boukerroui, A., Baskurt, A. M., Noble, J. A., & Basset, O. (2003) · Pattern Recognition Letters · doi:10.1016/s0167-8655(02)00181-2 · Cited by 139 · cited 8× · see citing works
“Texture and other local region features have more descriptive power than intensity, and have been studied in many works [9, 10, 64, 98] that they can distinguish tumor regions from normal tissues with high accuracy.”
BackgroundMethodInfluential
An easy measure of compactness for 2D and 3D shapes
Bribiesca, E. (2007) · Pattern Recognition · doi:10.1016/j.patcog.2007.06.029 · Cited by 127 · cited 1× · see citing works
“NBC produced a set of suspected lesions, and a two-phase lesion selection method based on region shape features (area size, width-height ratio, region ratio and compactness) and region continuity and volume size were applied for final tumor region decision [108].”
Method
A novel segmentation method for breast ultrasound images based on neutrosophic l‐means clustering
Shan, J., Cheng, H.-D., & Wang, Y. (2012) · Medical Physics · doi:10.1118/1.4747271 · Cited by 114 · cited 2× · see citing works
“[94, 95] extended the FCM and proposed the neutrosophic l-means (NLM) clustering which takes”
BackgroundMethodSelf-citation
Computerized lesion segmentation of breast ultrasound based on marker‐controlled watershed transformation
Gómez‐Flores, W., Leija, L., Alvarenga, A. V., Infantosi, A. F. C., & Pereira, W. C. D. A. (2009) · Medical Physics · doi:10.1118/1.3265959 · Cited by 112 · cited 4× · see citing works
“255 groups of markers were selected by thresholding (th = 1, 2, ⋯, 255) the image [124, 125]; the external and the internal markers were defined by using the morphological dilation and erosion.”
MethodInfluential
Development of a fully automatic scheme for detection of masses in whole breast ultrasound images
Ikedo, Y., Fukuoka, D., Hara, T., Fujita, H., Takada, E., Endo, T., & Morita, T. (2007) · Medical Physics · doi:10.1118/1.2795825 · Cited by 105 · cited 5× · see citing works
“Mean filter and Gaussian low-pass filter are simple and fast, and widely used in early BUS segmentation approaches [22, 52, 61, 59, 62, 63, 109, 115, 112, 123, 133, 138]; SRAD, NCD, sticks filter, and fractional subpixel diffusion are specially designed to deal with speckle noise, and because of their excellent proper…”
BackgroundMethodInfluential
Computerized detection and classification of cancer on breast ultrasound1
Drukker, K., Giger, M. L., Vyborny, C. J., & Mendelson, E. B. (2004) · Academic Radiology · doi:10.1016/s1076-6332(03)00723-2 · Cited by 105 · see citing works
A robust graph-based segmentation method for breast tumors in ultrasound images
Huang, Q.-H., Lee, S.-Y., Liu, L., Lu, M.-H., Jin, L.-W., & Li, A.-H. (2011) · Ultrasonics · doi:10.1016/j.ultras.2011.08.011 · Cited by 104 · cited 1× · see citing works
“Mean filter and Gaussian low-pass filter are simple and fast, and widely used in early BUS segmentation approaches [22, 52, 61, 59, 62, 63, 109, 115, 112, 123, 133, 138]; SRAD, NCD, sticks filter, and fractional subpixel diffusion are specially designed to deal with speckle noise, and because of their excellent proper…”
Method
A novel algorithm for initial lesion detection in ultrasound breast images
Yap, M. H., Edirisinghe, E. A., & Bez, H. (2008) · Journal of Applied Clinical Medical Physics · doi:10.1120/jacmp.v9i4.2741 · Cited by 100 · cited 2× · see citing works
“[110, 111] Global thresholding Pre-segmentation Fixed threshold Depended on image enhancement; could not adapt to variations of image quality.”
Background
3-D breast ultrasound segmentation using active contour model
Chen, D., Chang, R., Wu, W.-J., Moon, W. K., & Wu, W.-L. (2003) · Ultrasound in Medicine & Biology · doi:10.1016/s0301-5629(03)00059-0 · Cited by 99 · cited 3× · see citing works
“[62] and some early works [58-61] detected tumor automatically based on low-level image features, e.”
BackgroundMethod
A dual‐stage method for lesion segmentation on digital mammograms
Yuan, Y., Giger, M. L., Li, H., Suzuki, K., & Sennett, C. A. (2007) · Medical Physics · doi:10.1118/1.2790837 · Cited by 99 · cited 1× · see citing works
“The stopping criterion was defined as ( ̅ − ̅ ) − ( ̅ − ̅ ) = 0 [84], where ̅ and ̅ were the mean intensities inside and outside the segmented regions at step t, respectively.”
On Matching Deformable Models to Images
Terzopoulos, D. (1987) · doi:10.1364/mv.1987.fd1 · Cited by 96 · cited 2× · see citing works
“DMs are proposed by Terzopoulos [37], and then become a popular and active field after the snake approach for planar image proposed by Kass et al.”
Background
Active contour models: overview, implementation and applications
Menet, S., Saint-Marc, P., & Medioni, G. G. (2002) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.1990.142091 · Cited by 93 · see citing works
Multiple Resolution Bayesian Segmentation of Ultrasound Images
Ashton, E. A., & Parker, K. J. (1995) · Ultrasonic Imaging · doi:10.1177/016173469501700403 · Cited by 88 · cited 9× · see citing works
“[9] stated that healthy and pathological breast tissues present different textures of BUS images, and improved the method in [7] by modeling both intensity and texture distributions in the likelihood energy; they also assume that the texture features represented by using co-occurrence matrix follow the Gaussian distri…”
BackgroundMethodInfluential
A novel automatic seed point selection algorithm for breast ultrasound images
Shan, J., Cheng, H.-D., & Wang, Y. (2008) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2008.4761336 · Cited by 87 · cited 6× · see citing works
“Mean filter and Gaussian low-pass filter are simple and fast, and widely used in early BUS segmentation approaches [22, 52, 61, 59, 62, 63, 109, 115, 112, 123, 133, 138]; SRAD, NCD, sticks filter, and fractional subpixel diffusion are specially designed to deal with speckle noise, and because of their excellent proper…”
BackgroundMethodInfluentialSelf-citation
Multi-Dimensional Tumor Detection in Automated Whole Breast Ultrasound Using Topographic Watershed
Lo, C., Chen, R., Chang, Y., Yang, Y., Hung, M.-J., Huang, C., & Chang, R. (2014) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2315206 · Cited by 85 · cited 2× · see citing works
“[127] Watershed Pre-segmentation Used regions on the binary edge map as markers Depended on empirical rules to refine the results [128] Watershed Pre-segmentation Local intensity minima Depended on empirical rules to refine the results In this section, we present the theoretic background of three classical image segme…”
Method
Multiresolution texture based adaptive clustering algorithm for breast lesion segmentation
Djamal Boukerroui, A., Basset, O., Guérin, N., & Baskurt, A. (1998) · European Journal of Ultrasound · doi:10.1016/s0929-8266(98)00062-7 · Cited by 77 · cited 10× · see citing works
“[9] stated that healthy and pathological breast tissues present different textures of BUS images, and improved the method in [7] by modeling both intensity and texture distributions in the likelihood energy; they also assume that the texture features represented by using co-occurrence matrix follow the Gaussian distri…”
BackgroundMethodInfluential
Low-rank and sparse decomposition based shape model and probabilistic atlas for automatic pathological organ segmentation
Shi, C., Cheng, Y., Wang, J., Wang, Y., Mori, K., & Tamura, S. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.02.008 · Cited by 77 · cited 1× · see citing works
“, computed tomography (CT), magnetic resonance imaging (MRI), mammography, BUS image segmentation is very challenging because (1) ultrasound image has very low quality due to the speckle noise, low contrast, low single noise ratio (SNR) and artifacts; (2) large variations of breast structures exists among patients, wh…”
Background
Texture-Oriented Anisotropic Filtering and Geodesic Active Contours in Breast Tumor Ultrasound Segmentation
Alemán-Flores, M., Álvarez, L., & Caselles, V. (2007) · Journal of Mathematical Imaging and Vision · doi:10.1007/s10851-007-0015-8 · Cited by 70 · cited 2× · see citing works
“[33] proposed a semi-automatic BUS image segmentation method based on Normalized cut.”
Method
Computerized characterization of breast masses on three‐dimensional ultrasound volumes
Sahiner, B., Chan, H., Roubidoux, M. A., Helvie, M. A., Hadjiiski, L. M., Ramachandran, A., Paramagul, C., LeCarpentier, G. L., Nees, A. V., & Blane, C. E. (2004) · Medical Physics · doi:10.1118/1.1649531 · Cited by 70 · cited 1× · see citing works
“In [56, 57], Sahiner applied PDM for 3D BUS tumor segmentation, the external forces have two terms; the first term is defined on image gradient calculated using 3×3 Sobel filters; and the second term is the balloon force.”
Method
Tumor detection in automated breast ultrasound images using quantitative tissue clustering
Moon, W. K., Lo, C., Chen, R., Shen, Y., Chang, J. M., Huang, C., Chen, J., Hsu, W., & Chang, R. (2014) · Medical Physics · doi:10.1118/1.4869264 · Cited by 68 · cited 4× · see citing works
“[91] 2014 FCM F 148 High sensitive rate Sensitive to initialization; used fixed threshold 2.”
BackgroundMethodInfluential
Optimized graph-based segmentation for ultrasound images
Huang, Q., Bai, X. P., Li, Y., Jin, L., & Li, X. (2013) · Neurocomputing · doi:10.1016/j.neucom.2013.09.038 · Cited by 68 · cited 2× · see citing works
“Mean filter and Gaussian low-pass filter are simple and fast, and widely used in early BUS segmentation approaches [22, 52, 61, 59, 62, 63, 109, 115, 112, 123, 133, 138]; SRAD, NCD, sticks filter, and fractional subpixel diffusion are specially designed to deal with speckle noise, and because of their excellent proper…”
Method
Automated Segmentation of Ultrasonic Breast Lesions Using Statistical Texture Classification and Active Contour Based on Probability Distance
Liu, B., Cheng, H.-D., Huang, J., Tian, J., Liu, J., & Tang, X. (2009) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2008.12.007 · Cited by 65 · cited 6× · see citing works
“Texture and other local region features have more descriptive power than intensity, and have been studied in many works [9, 10, 64, 98] that they can distinguish tumor regions from normal tissues with high accuracy.”
BackgroundMethodInfluentialSelf-citation
A hierarchical local region-based sparse shape composition for liver segmentation in CT scans
Shi, C., Cheng, Y., Liu, F., Wang, Y., Bai, J., & Tamura, S. (2015) · Pattern Recognition · doi:10.1016/j.patcog.2015.09.001 · Cited by 64 · cited 1× · see citing works
“, computed tomography (CT), magnetic resonance imaging (MRI), mammography, BUS image segmentation is very challenging because (1) ultrasound image has very low quality due to the speckle noise, low contrast, low single noise ratio (SNR) and artifacts; (2) large variations of breast structures exists among patients, wh…”
Background
Segmentation of breast tumor in three-dimensional ultrasound images using three-dimensional discrete active contour model
Chang, R., Wu, W.-J., Moon, W. K., Chen, W., Lee, W., & Chen, D. (2003) · Ultrasound in Medicine & Biology · doi:10.1016/s0301-5629(03)00992-x · Cited by 64 · cited 2× · see citing works
“[62] and some early works [58-61] detected tumor automatically based on low-level image features, e.”
BackgroundMethod
A fast level set method for segmentation of low contrast noisy biomedical images
Deng, J., & Tsui, H. T. (2002) · Pattern Recognition Letters · doi:10.1016/s0167-8655(01)00113-1 · Cited by 52 · cited 2× · see citing works
“[63] proposed a fast GDM method for biomedical image segmentation.”
Method
Pattern Recognition and Image Analysis
Martínez-Usó, A., Sotoca, J. M., Pla, F., & Anaya-Sánchez, H. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-21257-4 · Cited by 52 · see citing works
Modeling and Control of Complex Dynamic Systems: Applied Mathematical Aspects
Gao, Z., Kong, D.-X., & Gao, C. (2012) · Journal of Applied Mathematics · doi:10.1155/2012/869792 · Cited by 46 · cited 2× · see citing works
“3, because of the speckle noise and week boundary problems, the performance of most approaches depends on both denoising and edge preservation techniques; [73, 75] defined the stop function of GDM for edge detection results in the frequency domain rather than in the spatial domain, which makes the GDM insensitive to i…”
BackgroundMethod
Cell-competition algorithm: A new segmentation algorithm for multiple objects with irregular boundaries in ultrasound images
Chen, C., Chou, Y., Chen, C. S. K., Cheng, J.-Z., Ou, Y.-F., Yeh, F.-C. F., & Chen, K. (2005) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2005.09.011 · Cited by 46 · cited 5× · see citing works
“Mean filter and Gaussian low-pass filter are simple and fast, and widely used in early BUS segmentation approaches [22, 52, 61, 59, 62, 63, 109, 115, 112, 123, 133, 138]; SRAD, NCD, sticks filter, and fractional subpixel diffusion are specially designed to deal with speckle noise, and because of their excellent proper…”
BackgroundMethodInfluential
Automatic Contouring for Breast Tumors in 2-D Sonography
Huang, Y.-L., & Chen, D. (2005) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2005.1617163 · Cited by 46 · cited 3× · see citing works
“[62] and some early works [58-61] detected tumor automatically based on low-level image features, e.”
BackgroundMethod
Computer-Aided Multiview Tumor Detection for Automated Whole Breast Ultrasound
Lo, C., Shen, Y.-W., Huang, C., & Chang, R. (2013) · Ultrasonic Imaging · doi:10.1177/0161734613507240 · Cited by 44 · cited 5× · see citing works
“Mean filter and Gaussian low-pass filter are simple and fast, and widely used in early BUS segmentation approaches [22, 52, 61, 59, 62, 63, 109, 115, 112, 123, 133, 138]; SRAD, NCD, sticks filter, and fractional subpixel diffusion are specially designed to deal with speckle noise, and because of their excellent proper…”
BackgroundMethodInfluential
Learning-based automatic breast tumor detection and segmentation in ultrasound images
Jiang, P., Peng, J., Zhang, G., Cheng, E., Megalooikonomou, V., & Ling, H. (2012) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2012.6235878 · Cited by 42 · cited 5× · see citing works
“[100] proposed two-step BUS segmentation approach.”
BackgroundMethodInfluential
A saliency model for automated tumor detection in breast ultrasound images
Shao, H., Zhang, Y., Xian, M., Cheng, H.-D., Xu, F., & Ding, J. (2015) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2015.7351035 · Cited by 41 · cited 2× · see citing works
“[22, 115, 154] obtain fully automatic BUS image segmentation modeling biological priors of BUS images or visual attention mechanism, which open new avenues for fully automatic BUS”
MethodSelf-citation
An Effective Approach of Lesion Segmentation Within the Breast Ultrasound Image Based on the Cellular Automata Principle
Liu, Y., Cheng, H.-D., Huang, J., Zhang, Y., & Tang, X. (2012) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-011-9450-6 · Cited by 40 · cited 2× · see citing works
“The category of the others [136, 138, 139, 140, 142-146] is composed of three small sub-categories, each contains only few literatures.”
BackgroundSelf-citation
Fractional subpixel diffusion and fuzzy logic approach for ultrasound speckle reduction
Zhang, Y., Cheng, H.-D., Tian, J., Huang, J., & Tang, X. (2010) · Pattern Recognition · doi:10.1016/j.patcog.2010.02.014 · Cited by 37 · cited 1× · see citing works
“, mean filter, Gaussian low-pass filter, speckle reducing anisotropic diffusion (SRAD) [148], nonlinear coherence diffusion (NCD) [149], sticks filter [60], bilateral filter [150, 151], fractional subpixel diffusion [157], etc.”
BackgroundSelf-citation
Phase‐ and GVF‐Based Level Set Segmentation of Ultrasonic Breast Tumors
Gao, L. S., Liu, X., & Chen, W. (2012) · Journal of Applied Mathematics · doi:10.1155/2012/810805 · Cited by 36 · see citing works
Morphological gradients
Rivest, J.-F., Soille, P., & Beucher, S. (1992) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.58373 · Cited by 35 · cited 1× · see citing works
“[124] Watershed Pre-segmentation Decided the external and internal markers by computing the Beucher gradient [131] of the morphological dilation and erosion of the binary image Depended on image preprocessing; select 255 groups of markers by thresholding image using thresholds from 0 to 255; needed additional geometri…”
Method
TurboPixel Segmentation Using Eigen-Images
Xiang, S., Pan, C., Nie, F., & Zhang, C. (2010) · IEEE Transactions on Image Processing · doi:10.1109/tip.2010.2052268 · Cited by 32 · cited 1× · see citing works
“For state-of-the-art superpixel generation approaches, refer [177-179].”
Multiple-domain knowledge based MRF model for tumor segmentation in breast ultrasound images
Xian, M., Huang, J., Zhang, Y., & Tang, X. (2012) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2012.6467286 · Cited by 30 · cited 1× · see citing works
“[23], 2015 Graph cuts F 184 images No distribution assumption; model the tumor pose, position; construct smoothness term in both spatial and frequency domains “shrink” problem”
BackgroundSelf-citation
Neutro-Connectedness Cut
Xian, M., Zhang, Y., Cheng, H.-D., Xu, F., & Ding, J. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2594485 · Cited by 30 · cited 1× · see citing works
“The intensity and sensitivity of interaction are two important criteria for evaluating interactive segmentation approaches [152, 153]; because user interaction has large degree of arbitrariness and different interaction may lead to quite different results.”
BackgroundSelf-citation
A Fully Automatic Breast Ultrasound Image Segmentation Approach Based on Neutro-Connectedness
Xian, M., Cheng, H.-D., & Zhang, Y. (2014) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2014.431 · Cited by 29 · cited 7× · see citing works
“Mean filter and Gaussian low-pass filter are simple and fast, and widely used in early BUS segmentation approaches [22, 52, 61, 59, 62, 63, 109, 115, 112, 123, 133, 138]; SRAD, NCD, sticks filter, and fractional subpixel diffusion are specially designed to deal with speckle noise, and because of their excellent proper…”
BackgroundMethodInfluentialSelf-citation
Automated segmentation of breast lesions in ultrasound images
Liu, X., Huo, Z., & Zhang, J. (2005) · doi:10.1109/iembs.2005.1616230 · Cited by 28 · cited 2× · see citing works
“[27, 34] applied normalized cut as an initial step to segment BUS images into small patches, and the final segmentation needs extra merging step and formulated knowledge to determine the tumor region.”
Method
Segmentation of ultrasonic breast tumors based on homogeneous patch
Gao, L. S., Yang, W., Liao, Z., Liu, X., Feng, Q., & Chen, W. (2012) · Medical Physics · doi:10.1118/1.4718565 · Cited by 27 · cited 2× · see citing works
“[32], 2012 Normalized cut S 100 images No shrink problem Manually select ROI”
Background
Cell-based dual snake model: a new approach to extracting highly winding boundaries in the ultrasound images
Chen, C., Lu, H. H., & Huang, Y.-S. (2002) · Ultrasound in Medicine & Biology · doi:10.1016/s0301-5629(02)00531-8 · Cited by 27 · cited 11× · see citing works
“Mean filter and Gaussian low-pass filter are simple and fast, and widely used in early BUS segmentation approaches [22, 52, 61, 59, 62, 63, 109, 115, 112, 123, 133, 138]; SRAD, NCD, sticks filter, and fractional subpixel diffusion are specially designed to deal with speckle noise, and because of their excellent proper…”
MethodInfluential
Level Set Segmentation of Breast Masses in Contrast-Enhanced Dedicated Breast CT and Evaluation of Stopping Criteria
Kuo, H.-C., Giger, M. L., Reiser, I., Boone, J. M., Lindfors, K. K., Yang, K., & Edwards, A. V. (2013) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-013-9652-1 · Cited by 26 · cited 1× · see citing works
“[81, 82] proposed a semi-automatic approach for 3D BUS image segmentation.”
Whole Breast Lesion Detection Using Naive Bayes Classifier for Portable Ultrasound
Yang, M.-C., Huang, C., Chen, J., & Chang, R. (2012) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2012.07.006 · Cited by 26 · cited 1× · see citing works
“[107], 2012 NBC F 31 cases (Whole BUS images) Achieved high sensitive ratio Depended on the assumption of intensity distribution; depended on post selection to reduce false positive ratio 4.”
Background
Breast-lesion Segmentation Combining B-Mode and Elastography Ultrasound
Pons, G., Martı́, J., Martí, R., Ganau, S., & Noble, J. A. (2015) · Ultrasonic Imaging · doi:10.1177/0161734615589287 · Cited by 26 · cited 2× · see citing works
“The number of classes is usually set from 2 to 4 for BUS image; and β could be set adaptively [7] or set to a constant [11-13]; and the parameters of Gaussian distribution are usually initialized by using K-means algorithm [7, 9, 10].”
BackgroundMethod
Fully automatic lesion boundary detection in ultrasound breast images
Yap, M. H., Edirisinghe, E. A., & Bez, H. (2007) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.708625 · Cited by 25 · cited 2× · see citing works
“[110, 111] Global thresholding Pre-segmentation Fixed threshold Depended on image enhancement; could not adapt to variations of image quality.”
Background
Segmentation of ultrasound breast images based on a neutrosophic method
Zhang, M., Zhang, L., & Cheng, H.-D. (2010) · Optical Engineering · doi:10.1117/1.3505854 · Cited by 23 · cited 2× · see citing works
“[127] Watershed Pre-segmentation Used regions on the binary edge map as markers Depended on empirical rules to refine the results [128] Watershed Pre-segmentation Local intensity minima Depended on empirical rules to refine the results In this section, we present the theoretic background of three classical image segme…”
MethodSelf-citation
Multiscale superpixel classification for tumor segmentation in breast ultrasound images
Hao, Z., Wang, Q., Ren, H., Xu, K., Seong, Y. K., & Kim, J.-Y. (2012) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2012.6467485 · Cited by 21 · cited 3× · see citing works
“[27, 34] applied normalized cut as an initial step to segment BUS images into small patches, and the final segmentation needs extra merging step and formulated knowledge to determine the tumor region.”
Method
Segmentation of breast masses on dedicated breast computed tomography and three-dimensional breast ultrasound images
Kuo, H.-C., Giger, M. L., Reiser, I., Drukker, K., Boone, J. M., Lindfors, K. K., Yang, K., Edwards, A. V., & Sennett, C. A. (2014) · Journal of medical imaging · doi:10.1117/1.jmi.1.1.014501 · Cited by 21 · cited 1× · see citing works
“[81, 82] proposed a semi-automatic approach for 3D BUS image segmentation.”
EISeg: Effective interactive segmentation
Xian, M., Xu, F., Cheng, H.-D., Zhang, Y., & Ding, J. (2016) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2016.7899927 · Cited by 21 · cited 3× · see citing works
“Human vision system is very sensitive to the topological properties of objects, and some works have been investigated [115, 152, 153] for both natural and medical image processing tasks.”
BackgroundMethodSelf-citation
Segmentation of Breast Ultrasound Images Using Neural Networks
Othman, A. A., & Tizhoosh, H. R. (2011) · IFIP International Federation for Information Processing/IFIP · doi:10.1007/978-3-642-23957-1_30 · Cited by 20 · cited 1× · see citing works
“[104] trained an ANN to generate the threshold for BUS image segmentation.”
Method
Automatic boundary extraction of ultrasonic breast lesions
Madabhushi, A., & Metaxas, D. (2003) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2002.1029329 · Cited by 20 · cited 4× · see citing works
“The approaches [54, 55, 113, 106] constructing empirical formulas to model domain priors provided effective means for tumor detection, but the fixed RP formulated in these approaches limited their robustness and flexibility.”
BackgroundMethodInfluential
Neural network analysis applied to tumor segmentation on 3D breast ultrasound images
Huang, S.-F., Chen, Y.-C., & Moon, W. K. (2008) · IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI) · doi:10.1109/isbi.2008.4541243 · Cited by 20 · cited 4× · see citing works
“35 / 71 [103] ANN F 93 cases (3D) Fully automatic Depended on fixed threshold to produce candidate regions; relatively low performance [106], 2012 ANN F 120 images Achieved good performance by using the phase information, radial distance and the joint distribution of texture and intensity Depended on fixed reference p…”
BackgroundMethodInfluential
Database-guided breast tumor detection and segmentation in 2D ultrasound images
Zhang, J., Zhou, S. K., Brunke, S. S., Lowery, C., & Comaniciu, D. (2010) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.844558 · Cited by 19 · cited 9× · see citing works
“In [22, 54, 55], no explicit distribution was predefined, and histogram was applied to describe the distribution of tumor region and normal tissues; In [25, 27, 28], supervised learning approaches are introduced to train the classifiers to output the probability of each image region to be tumor or background.”
BackgroundMethodInfluential
Cell-Based Two-Region Competition Algorithm with a Map Framework for Boundary Delineation of a Series of 2D Ultrasound Images
Cheng, J.-Z., Chen, C., Chou, Y., Chen, C. S. K., Tiu, C., & Chen, K. (2007) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2007.04.019 · Cited by 19 · cited 2× · see citing works
“[124], 2012 PDMs S 20 Redefine the edge-based stopping function using phase information Validated only on a small dataset; sensitive to noise [139], 2013 GDMs S 168 Use both local statistics and phase information to define the speed function; and handle weak boundary and inhomogeneity problems better Slow”
Method
An Automatic Localization Algorithm for Ultrasound Breast Tumors Based on Human Visual Mechanism
Xie, Y., Chen, K., & Lin, J. (2017) · Sensors · doi:10.3390/s17051101 · Cited by 18 · cited 1× · see citing works
“Potential directions are to learn invariant and discriminative representations of tumors in BUS images, and to model human vision mechanism [110, 154, 176].”
Background
Novel Approaches to Image Segmentation Based on Neutrosophic Logic
Zhang, M. (2021) · Digital Commons - USU (Utah State University) · doi:10.26076/3db7-64f2 · Cited by 17 · see citing works
RD-Based Seeded Region Growing for Extraction of Breast Tumor in an Ultrasound Volume
Kwak, J. I., Kim, S., & Kim, N. C. (2005) · Lecture notes in computer science · doi:10.1007/11596448_118 · Cited by 16 · cited 3× · see citing works
“[117] defined the cost of growing a region by modelling common contour smoothness and region similarity (mean intensity and size): ( , ) = ( ) − ( ) ∙ ( , ) + ∙ ( ) ∙ ( ) ( ) + ( ) , ∈ ( ) (39) where (∙) denotes the mean intensity of a region, (∙) is the pixel number of the region, ( , ) is the length of the common co…”
Background
Active contour driven by local divergence energies for ultrasound image segmentation
Yuan, J. (2013) · IET Image Processing · doi:10.1049/iet-ipr.2012.0461 · Cited by 16 · cited 1× · see citing works
“[80] proposed a new level set based DM approach; the local divergence and a smoothing kernel were introduced to improve the speed function.”
Method
Lesion Segmentation in Breast Sonography
Massich, J., Mériaudeau, F., Pérez, E., Martí, R., Oliver, A., & Martı́, J. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-13666-5_6 · Cited by 16 · cited 2× · see citing works
“[118] used this method for seed generation.”
Background
Continuous force field analysis for generalized gradient vector flow field
Rodtook, A., & Makhanov, S. S. (2010) · Pattern Recognition · doi:10.1016/j.patcog.2010.04.003 · Cited by 15 · cited 2× · see citing works
“No quantitative evaluation of the segmentation [59], 2007 PDMS F 360 Use GVF to extend the attraction range and to handle concave boundaries Fixed threshold and reference point are applied for initial boundary generation; no quantitative evaluation [69], 2009 PDMs F 103 Use a well-trained texture classifier to detect…”
Method
A phase-based active contour model for segmentation of breast ultrasound images
Cai, L., & Wang, Y. (2013) · International Conference on Biomedical Engineering and Informatics (BMEI) · doi:10.1109/bmei.2013.6746913 · Cited by 14 · cited 2× · see citing works
“3, because of the speckle noise and week boundary problems, the performance of most approaches depends on both denoising and edge preservation techniques; [73, 75] defined the stop function of GDM for edge detection results in the frequency domain rather than in the spatial domain, which makes the GDM insensitive to i…”
BackgroundMethod
Cell-based graph cut for segmentation of 2D/3D sonographic breast images
Chiang, H.-H., Cheng, J., Hung, P., Liu, C.-Y., Chung, C.-H., & Chen, C. (2010) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2010.5490384 · Cited by 14 · cited 1× · see citing works
“[140] extended the approach to segment 3D BUS image, and applied Graph cuts to image regions for finding the final tumor boundary.”
Method
Accurate Segmentation of Breast Tumors in Ultrasound Images Using a Custom-Made Active Contour Model and Signal-to-Noise Ratio Variations
Daoud, M. I., Baba, M. M., Awwad, F. R., Al-Najjar, M., & Tarawneh, E. S. (2012) · International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) · doi:10.1109/sitis.2012.30 · Cited by 12 · cited 1× · see citing works
“[71] considered the SNR and local intensity value as two important features for estimating tumor boundary, and built a two-fold termination criterion based on the two features in discrete dynamic DMs [72].”
Method
A robust region-based active contour model with point classification for ultrasound breast lesion segmentation
Liu, Z., Zhang, L., Ren, H., & Kim, J.-Y. (2013) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2006164 · Cited by 11 · cited 2× · see citing works
“[79] adopted the region-based approach in [78], proposed a learning based approach (multivariate linear regression and support vector regression) to produce the parameters adaptively, and proved better performance using 481 BUS images.”
BackgroundMethod
Completely automatic segmentation for breast ultrasound using multiple-domain features
Shan, J., Wang, Y., & Cheng, H.-D. (2010) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2010.5652626 · Cited by 11 · cited 3× · see citing works
“4 / 40 signed to deal with speckle noise, and because of their excellent property in edge preservation, these approaches are employed in BUS segmentation popularly [34, 58, 65, 89, 95, 103, 105, 106, 113, 114, 124, 125, 158, 165, 166].”
MethodSelf-citation
Active Contours without Edges Applied to Breast Lesions on Ultrasound
Gómez‐Flores, W., Infantosi, A. F. C., Leija, L., & Pereira, W. C. D. A. (2010) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-642-13039-7_73 · Cited by 10 · cited 2× · see citing works
“Mean filter and Gaussian low-pass filter are simple and fast, and widely used in early BUS segmentation approaches [22, 52, 61, 59, 62, 63, 109, 115, 112, 123, 133, 138]; SRAD, NCD, sticks filter, and fractional subpixel diffusion are specially designed to deal with speckle noise, and because of their excellent proper…”
BackgroundMethod
A modified spatial fuzzy clustering method based on texture analysis for ultrasound image segmentation
Xu, Y. L. (2009) · IEEE International Symposium on Industrial Electronics (ISIE) · doi:10.1109/isie.2009.5213933 · Cited by 9 · cited 1× · see citing works
“[87] proposed a BUS image segmentation method using the spatial FCM (sFCM) [88] with local texture and intensity features.”
Method
Simultaneous Lesion Segmentation and Bias Correction in Breast Ultrasound Images
Pons, G., Martı́, J., Martí, R., & Noble, J. A. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-21257-4_86 · Cited by 8 · cited 2× · see citing works
“In order to improve the process of manual selection of Gaussian parameters in [11], reference [12] proposed a one-click user interaction to estimate Gaussian parameters automatically.”
Method
Segmentation of Breast Nodules on Ultrasonographic Images Based on Marke d-Controlled Watershed Transform
Gómez, W., Leija, L., Pereira, W. C. A., & Infantosi, A. F. C. (2010) · Redalyc (Universidad Autónoma del Estado de México) · Cited by 7 · see citing works
3D segmentation of breast tumor in ultrasound images
Kwak, J. I., Jung, M. N., Kim, S. H., & Kim, N. C. (2003) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.479903 · Cited by 7 · cited 1× · see citing works
“Seed generation: the seeds can be placed by user interactively [116, 117] or generated automatically [54, 55, 113, 118].”
Background
Automatic 3D lesion segmentation on breast ultrasound images
Kuo, H.-C., Giger, M. L., Reiser, I., Drukker, K., Edwards, A. V., & Sennett, C. A. (2013) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2008014 · Cited by 7 · cited 1× · see citing works
“[145, 146] applied RGI to find the initial tumor contour of 3D BUS image.”
Method
Unsupervised saliency estimation based on robust hypotheses
Xu, F., Xian, M., Cheng, H.-D., Ding, J., & Zhang, Y. (2016) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2016.7477623 · Cited by 7 · cited 3× · see citing works
“Potential directions are to learn invariant and discriminative representations of tumors in BUS images, and to model human vision mechanism [110, 154, 176].”
BackgroundSelf-citation
Computer-aided detection system of breast masses on ultrasound images
Ikedo, Y., Fukuoka, D., Hara, T., Fujita, H., Takada, E., Endo, T., & Morita, T. (2006) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.654430 · Cited by 5 · cited 2× · see citing works
“[109, 123] used watershed to segment ROI into small regions, and used the predefined criteria (area, mean intensity, geodesic center, etc) to determine the final tumor region.”
Method
Ultrasound lesion segmentation using clinical knowledge-driven constrained level set
Lin, Q., Liu, S., Parajuly, S. S., Deng, Y., Boroczky, L., Fu, S., Wu, Y. N., & Pen, Y. (2013) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2013.6610936 · Cited by 4 · cited 1× · see citing works
“[76] modified the local region-based level set approach [77] by using additional constrained energies centered at four markers specified by radiologist.”
Method
Learning a Structured Graphical Model with Boosted Top-Down Features for Ultrasound Image Segmentation
Hao, Z., Wang, Q., Wang, X., Kim, J. B., Hwang, Y., Cho, B. H., Guo, P. Y., & Lee, W. K. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-40811-3_29 · Cited by 4 · cited 5× · see citing works
“In [28], both the region-level [29] and pixellevel features were utilized to define the weights in the data term; and all weights were learned by using a structural SVM [30].”
BackgroundMethodInfluential
Computerized detection and classification of lesions on breast ultrasound
Drukker, K., Giger, M. L., Vyborny, C. J., Schmidt, R. A., Mendelson, E. B., & Stern, M. (2003) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.480856 · Cited by 3 · cited 1× · see citing works
“In [143, 144], a NN with five 5 hidden units was used to classify the candidate lesions into true positive and false positive; Kuo et al.”
Method
Computerized analysis of sonograms for the detection of breast lesions
Drukker, K., Giger, M. L., Horsch, K., & Vyborny, C. J. (2002) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.467093 · Cited by 3 · cited 1× · see citing works
“In [143, 144], a NN with five 5 hidden units was used to classify the candidate lesions into true positive and false positive; Kuo et al.”
Method
Ultrasound breast lesion segmentation using adaptive parameters
Cho, B. H., Seong, Y. K., Kim, J., Liu, Z., Hao, Z., Ko, E. Y., & Woo, K.-G. (2014) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2041893 · Cited by 3 · cited 1× · see citing works
“[79] adopted the region-based approach in [78], proposed a learning based approach (multivariate linear regression and support vector regression) to produce the parameters adaptively, and proved better performance using 481 BUS images.”
Method
Adaptive expanding B-snake model for extracting ultrasound breast lump boundary
Yintao, C., Keong, K. C., Siew-Bock, W., & Zou, Q. (2001) · Australian and New Zealand Intelligent Information Systems Conference (ANZIIS) · doi:10.1109/anziis.2001.974042 · Cited by 2 · see citing works
Three-dimensional active contour model for characterization of solid breast masses on three-dimensional ultrasound images
Sahiner, B., Ramachandran, A., Chan, H., Roubidoux, M. A., Hadjiiski, L. M., Helvie, M. A., Petrick, N., & Zhou, C. (2003) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.483548 · Cited by 1 · cited 1× · see citing works
“In [56, 57], Sahiner applied PDM for 3D BUS tumor segmentation, the external forces have two terms; the first term is defined on image gradient calculated using 3×3 Sobel filters; and the second term is the balloon force.”
Method

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Sorted by citations

Deep Learning in Medical Ultrasound Analysis: A Review
Liu, S., Wang, Y., Yang, X., Lei, B., Liu, L., Li, S. X., Ni, D., & Wang, T. (2019) · Engineering · doi:10.1016/j.eng.2018.11.020 · Cited by 798 · see citing works
“However, few focus on medical US analysis, aside from one or two papers that examine speciﬁc tasks such as breast US image segmentation [55].”
Background
AAU-Net: An Adaptive Attention U-Net for Breast Lesions Segmentation in Ultrasound Images
Chen, G., Li, L., Dai, Y., Zhang, J., & Yap, M. H. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3226268 · Cited by 402 · see citing works
“The segmentation of breast ultrasound (BUS) images can help us characterize and localize breast tumors, which is one of the key steps in computer-aided diagnosis (CAD) [4].”
Background
Breast mass segmentation in ultrasound with selective kernel U-Net convolutional neural network
Byra, M., Jarosik, P., Szubert, A., Galperin, M. Y., Ojeda‐Fournier, H., Olson, L. K., O'Boyle, M. K., Comstock, C., & André, M. P. (2020) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2020.102027 · Cited by 328 · see citing works
“However, automatic segmentation in US imaging is considered a challenging task due to relatively low US image contrast, speckle noise and large variations in breast mass sizes and shapes [3,4].”
Background
Deep learning in ultrasound imaging
van Sloun, R. J. G., Cohen, R., & Eldar, Y. C. (2020) · Proceedings of the IEEE · doi:10.1109/jproc.2019.2932116 · Cited by 316 · see citing works
“Similar applications of deep learning include automated identification and segmentation of tumors in breast ultrasound [106], [107], [108], localization of clinically relevant B-line artifacts in lung ultrasonography [109], and”
Background
Segmentation of breast ultrasound image with semantic classification of superpixels
Huang, Q., Huang, Y., Luo, Y., Yuan, F., & Li, X. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101657 · Cited by 216 · see citing works
Attention-Enriched Deep Learning Model for Breast Tumor Segmentation in Ultrasound Images
Vakanski, A., Xian, M., & Freer, P. E. (2020) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2020.06.015 · Cited by 198 · see citing works
Self-citation
Rethinking the unpretentious U-net for medical ultrasound image segmentation
Chen, G., Li, L., Zhang, J., & Dai, Y. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.109728 · Cited by 200 · see citing works
CAD and AI for breast cancer—recent development and challenges
Chan, H., Samala, R. K., & Hadjiiski, L. M. (2019) · British Journal of Radiology · doi:10.1259/bjr.20190580 · Cited by 174 · see citing works
SMU-Net: Saliency-Guided Morphology-Aware U-Net for Breast Lesion Segmentation in Ultrasound Image
Ning, Z., Zhong, S., Feng, Q., Chen, W., & Zhang, Y. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3116087 · Cited by 171 · see citing works
“Currently, ultrasound imaging is a well-established tool used for early evaluation and diagnosis of abnormal breast due to its noninvasive imaging procedure, high sensitivity and low cost [3], [4].”
BackgroundMethodSelf-citation
TTCNN: A Breast Cancer Detection and Classification towards Computer-Aided Diagnosis Using Digital Mammography in Early Stages
Maqsood, S., Damaševičius, R., & Maskeliūnas, R. (2022) · Applied Sciences · doi:10.3390/app12073273 · Cited by 165 · see citing works
“Breast cancer has been among the major cause of death among women worldwide [1,2].”
Background
BUS‐BRA: A breast ultrasound dataset for assessing computer‐aided diagnosis systems
Gómez‐Flores, W., Calas, M. J. G., & Pereira, W. C. D. A. (2023) · Medical Physics · doi:10.1002/mp.16812 · Cited by 166 · see citing works
Sparse autoencoder for unsupervised nucleus detection and representation in histopathology images
Hou, L., Nguyen, V., Kanevsky, A. B., Samaras, D., Kurç, T., Zhao, T., Gupta, R., Gao, Y., Chen, W., Foran, D. J., et al. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.09.007 · Cited by 160 · see citing works
CSwin-PNet: A CNN-Swin Transformer combined pyramid network for breast lesion segmentation in ultrasound images
Yang, H., & Yang, D. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119024 · Cited by 158 · see citing works
Segmentation information with attention integration for classification of breast tumor in ultrasound image
Luo, Y., Huang, Q., & Li, X. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108427 · Cited by 149 · see citing works
A review on image-based approaches for breast cancer detection, segmentation, and classification
Rezaei, Z. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115204 · Cited by 137 · see citing works
Computer-aided detection of breast cancer on the Wisconsin dataset: An artificial neural networks approach
Alshayeji, M. H., Ellethy, H., Abed, S., & Gupta, R. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.103141 · Cited by 140 · see citing works
What is new in computer vision and artificial intelligence in medical image analysis applications
Olveres, J., González, G., Torres-Robles, F., Moreno-Tagle, J. C., Carbajal-Degante, E., Valencia-Rodríguez, A., Méndez‐Sánchez, N., & Ramírez, B. E. (2021) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-20-1151 · Cited by 124 · see citing works
“CAD systems using BUS images generally focus on solving three problems: lesion detection, lesion segmentation, and lesion classification.”
MethodInfluential
Fine-Tuning U-Net for Ultrasound Image Segmentation: Different Layers, Different Outcomes
Amiri, M., Brooks, R., & Rivaz, H. J. (2020) · IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control · doi:10.1109/tuffc.2020.3015081 · Cited by 122 · see citing works
“watershed-based, graph-based, active contour model, Markov random field, and classic machine learning methods [8], [9].”
Method
Ultrasound Image-Based Diagnosis of Malignant Thyroid Nodule Using Artificial Intelligence
Nguyen, D. T., Kang, J. K., Pham, T. D., Batchuluun, G., & Park, K. R. (2020) · Sensors · doi:10.3390/s20071822 · Cited by 119 · see citing works
“Since each CNN model has its own architecture and characteristics of learning the characteristics of input images, the use of multiple CNN-based models can help to extract richer information compared to using an individual model.”
Method
Deep Learning to Obtain Simultaneous Image and Segmentation Outputs From a Single Input of Raw Ultrasound Channel Data
Nair, A. A., Washington, K., Tran, T. D., Reiter, A., & Bell, M. A. L. (2020) · IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control · doi:10.1109/tuffc.2020.2993779 · Cited by 117 · see citing works
“Third, segmentation tasks are traditionally implemented after image formation [5], [6], which further increases the computational complexity of implementing segmentation algorithms to provide the desired segmentation result.”
BackgroundMethod
Advancements in Deep Learning for B-Mode Ultrasound Segmentation: A Comprehensive Review
Ansari, M. Y., Mangalote, I. A. C., Meher, P. K., Aboumarzouk, O. M., Al-Ansari, A. A., Halabi, O., & Dakua, S. P. (2024) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2024.3377676 · Cited by 116 · see citing works
“In recent years, some review papers on ML-based methods have been published for medical image analysis focusing on CT, MRI, or US [28], [29], [30], [31], [32], [33].”
Background
Breast tumor segmentation in 3D automatic breast ultrasound using Mask scoring R‐CNN
Lei, Y., He, X., Yao, J., Wang, T., Wang, L., Li, W., Curran, W. J., Liu, T., Xu, D. M., & Yang, X. (2020) · Medical Physics · doi:10.1002/mp.14569 · Cited by 114 · see citing works
Stan: Small Tumor-Aware Network for Breast Ultrasound Image Segmentation
Shareef, B., Xian, M., & Vakanski, A. (2020) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi45749.2020.9098691 · Cited by 111 · see citing works
“Refer to [20] for a detailed review of BUS segmentation approaches.”
BackgroundSelf-citation
Breast tumor localization and segmentation using machine learning techniques: Overview of datasets, findings, and methods
Ranjbarzadeh, R., Dorosti, S., Ghoushchi, S. J., Caputo, A., Tırkolaee, E. B., Ali, S. S., Arshadi, Z., & Bendechache, M. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106443 · Cited by 103 · see citing works
A comparative study of pre-trained convolutional neural networks for semantic segmentation of breast tumors in ultrasound
Gómez‐Flores, W., & Pereira, W. C. D. A. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.104036 · Cited by 105 · see citing works
Boundary-rendering network for breast lesion segmentation in ultrasound images
Huang, R., Lin, M., Dou, H., Lin, Z., Ying, Q., Jia, X., Xu, W., Mei, Z., Yang, X., Dong, Y., et al. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102478 · Cited by 102 · see citing works
ESKNet: An enhanced adaptive selection kernel convolution for ultrasound breast tumors segmentation
Chen, G., Zhou, L., Zhang, J., Yin, X., Cui, L., & Dai, Y. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.123265 · Cited by 105 · see citing works
“Regular early screening is critical to develop the medical programme and reduce the mortality rate due to the strong concealment and many inducements of breast cancer (Xian et al., 2018).”
Background
BUSIS: A Benchmark for Breast Ultrasound Image Segmentation
Zhang, Y., Xian, M., Cheng, H.-D., Shareef, B., Ding, J., Xu, F., Huang, K., Zhang, B., Ning, C., & Wang, Y. (2022) · Healthcare · doi:10.3390/healthcare10040729 · Cited by 102 · see citing works
“Many semi-automatic segmentation approaches are utilized for BUS image segmentation [27].”
BackgroundMethodInfluentialSelf-citation
C-Net: Cascaded convolutional neural network with global guidance and refinement residuals for breast ultrasound images segmentation
Chen, G., Dai, Y., & Zhang, J. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.107086 · Cited by 97 · see citing works
Semi-supervised segmentation of lesion from breast ultrasound images with attentional generative adversarial network
Han, L., Huang, Y., Dou, H., Wang, S., Ahamad, S., Luo, H., Liu, Q., Fan, J., & Zhang, J. (2019) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2019.105275 · Cited by 95 · see citing works
Efficient Breast Cancer Diagnosis from Complex Mammographic Images Using Deep Convolutional Neural Network
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Artificial Intelligence-Based Thyroid Nodule Classification Using Information from Spatial and Frequency Domains
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A survey on image segmentation of blood and bone marrow smear images with emphasis to automated detection of Leukemia
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Accurate segmentation of breast tumors using AE U-net with HDC model in ultrasound images
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Self-citation
RCA-IUnet: a residual cross-spatial attention-guided inception U-Net model for tumor segmentation in breast ultrasound imaging
Punn, N. S., & Agarwal, S. (2022) · Machine Vision and Applications · doi:10.1007/s00138-022-01280-3 · Cited by 93 · see citing works
“Following this context, deep learning-based computer-aided diagnosis (CAD) systems are developed for the early detection of breast tumor for faster diagnosis and treatment [3].”
BackgroundInfluential
Ultrasound Medical Imaging Techniques
Avola, D., Cinque, L., Fagioli, A., Foresti, G. L., & Mecca, A. (2021) · ACM Computing Surveys · doi:10.1145/3447243 · Cited by 95 · see citing works
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Background
HAU-Net: Hybrid CNN-transformer for breast ultrasound image segmentation
Zhang, H., Lian, J., Yi, Z., Wu, R., Lu, X., Ma, P., & Ma, Y. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105427 · Cited by 90 · see citing works
PDF-UNet: A semi-supervised method for segmentation of breast tumor images using a U-shaped pyramid-dilated network
Iqbal, A., & Sharif, M. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.119718 · Cited by 86 · see citing works
SHA-MTL: soft and hard attention multi-task learning for automated breast cancer ultrasound image segmentation and classification
Zhang, G., Zhao, K., Hong, Y., Qiu, X., Zhang, K., & Wei, B. (2021) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-021-02445-7 · Cited by 82 · see citing works
ESTAN: Enhanced Small Tumor-Aware Network for Breast Ultrasound Image Segmentation
Shareef, B., Vakanski, A., Freer, P. E., & Xian, M. (2022) · Healthcare · doi:10.3390/healthcare10112262 · Cited by 83 · see citing works
“We use three public BUS datasets: BUSIS [20,26,38,39], BUSI [40] and Dataset B [3].”
MethodInfluentialSelf-citation
Uncertainty-Aware Hierarchical Aggregation Network for Medical Image Segmentation
Zhou, T., Zhou, Y., Li, G., Chen, G., & Shen, J. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3370685 · Cited by 74 · see citing works
“…[1], such as tumor segmentation in magnetic resonance (MR) images [2], polyp segmentation in colonoscopy images [3], breast lesion segmentation in ultrasound images [4], lung infection segmentation in computed tomography (CT) Images [5], nuclei segmentation in microscope cell images [6], and so on.”
Background
Multi-Level Fusion in Ultrasound for Cancer Detection based on Uniform LBP Features
Zeebaree, D. Q., Abdulazeez, A. M., Zebari, D. A., Haron, H., & Hamed, H. N. A. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2021.013314 · Cited by 73 · see citing works
MRFE-CNN: multi-route feature extraction model for breast tumor segmentation in Mammograms using a convolutional neural network
Ranjbarzadeh, R., Sarshar, N. T., Ghoushchi, S. J., Esfahani, M. S., Parhizkar, M., Pourasad, Y., Anari, S., & Bendechache, M. (2022) · Annals of Operations Research · doi:10.1007/s10479-022-04755-8 · Cited by 73 · see citing works
“Due to the importance of early cancer detection, designing computer-aided design (CAD) systems can reduce the risk of life-threatening parts of disease (Xian et al., 2018; Abdelhaﬁz et al., 2019).”
Background
MCRNet: Multi-level context refinement network for semantic segmentation in breast ultrasound imaging
Lou, M., Meng, J., Qi, Y., Li, X., & Ma, Y. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.10.102 · Cited by 70 · see citing works
A Regional-Attentive Multi-Task Learning Framework for Breast Ultrasound Image Segmentation and Classification
Xu, M., Huang, K., & Qi, X. (2023) · IEEE Access · doi:10.1109/access.2023.3236693 · Cited by 71 · see citing works
“We employ commonly-used BUS segmentation metrics [9], [15], [17], [18], [30], [31] including sensitivity (SEN), specificity (SPE), accuracy (ACC), dice similarity coefficient (DSC), and intersection over the union of tumor (tumor IoU) to quantitatively evaluate the segmentation performance.”
Method
A method for segmentation of tumors in breast ultrasound images using the variant enhanced deep learning
Ilesanmi, A. E., Chaumrattanakul, U., & Makhanov, S. S. (2021) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2021.05.007 · Cited by 66 · see citing works
Deep weakly-supervised breast tumor segmentation in ultrasound images with explicit anatomical constraints
Li, Y., Liu, Y., Huang, L., Wang, Z., & Luo, J. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102315 · Cited by 66 · see citing works
Automatic superpixel-based segmentation method for breast ultrasound images
Daoud, M. I., Atallah, A. A., Awwad, F. R., Al-Najjar, M., & Alazrai, R. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.11.024 · Cited by 65 · see citing works
Accurate and automatic tooth image segmentation model with deep convolutional neural networks and level set method
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DSEU-net: A novel deep supervision SEU-net for medical ultrasound image segmentation
Chen, G., Liu, Y., Jiang, Q., Zhang, J., Yin, X., Cui, L., & Dai, Y. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.119939 · Cited by 65 · see citing works
Ultrasound image segmentation using a novel multi-scale Gaussian kernel fuzzy clustering and multi-scale vector field convolution
Panigrahi, L., Verma, K., & Singh, B. K. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.08.013 · Cited by 64 · see citing works
RRCNet: Refinement residual convolutional network for breast ultrasound images segmentation
Chen, G., Dai, Y., & Zhang, J. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105601 · Cited by 64 · see citing works
Semantic segmentation of breast ultrasound image with fuzzy deep learning network and breast anatomy constraints
Huang, K., Zhang, Y., Cheng, H., Xing, P., & Zhang, B. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.04.012 · Cited by 71 · see citing works
Self-citation
MDF-Net: A Multi-Scale Dynamic Fusion Network for Breast Tumor Segmentation of Ultrasound Images
Qi, W., Wu, H. C., & Chan, S. C. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3304518 · Cited by 60 · see citing works
“However, low contrast and image intensity of BUS, intersubject variability, and speckle noise still present great challenge to cancer tissue segmentation [15], [16], [17] (c.”
Background
Weakly-supervised deep learning for ultrasound diagnosis of breast cancer
Kim, J., Kim, H. J., Kim, C., Lee, J. H., Kim, K. W., Park, Y. M., Ki, S. Y., Ki, S. Y., Kim, Y. M., & Kim, W. H. (2021) · Scientific Reports · doi:10.1038/s41598-021-03806-7 · Cited by 61 · see citing works
“Automated segmentation has been suggested in various techniques including graph-based approaches, deformable models, intensity-based thresh-olding, region growing, and watershed algorithm 28 .”
Method
BIRADS features-oriented semi-supervised deep learning for breast ultrasound computer-aided diagnosis
Zhang, E., Seiler, S. J., Chen, M., Lu, W., & Gu, X. (2020) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ab7e7d · Cited by 61 · see citing works
Identification of Breast Malignancy by Marker-Controlled Watershed Transformation and Hybrid Feature Set for Healthcare
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“Global thresholding cannot perform well in noisy and low contrast images [10].”
BackgroundMethod
Improved U-net MALF model for lesion segmentation in breast ultrasound images
Tong, Y., Liu, Y., Zhao, M., Meng, L., & Zhang, J. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102721 · Cited by 58 · see citing works
A systematic survey of deep learning in breast cancer
Yu, X., Zhou, Q., Wang‎, S., & Zhang, Y. (2021) · International Journal of Intelligent Systems · doi:10.1002/int.22622 · Cited by 54 · see citing works
“In breast abnormality segmentation, numerous works deployed classical methods, but the exploration of utilizing deep learning models is quite limited.(167) Compared to traditional segmentation methods, CNN‐based segmentation methods, mainly FCNNs, turn out to 198 | YU ET AL.”
MethodSelf-citation
BT-Unet: A self-supervised learning framework for biomedical image segmentation using barlow twins with U-net models
Punn, N. S., & Agarwal, S. (2021) · Machine-mediated learning · doi:10.1007/s10994-022-06219-3 · Cited by 58 · see citing works
Fully automatic tumor segmentation of breast ultrasound images with deep learning
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“6,7 Image segmentation aims to mark the abnormal regions from the background.”
BackgroundSelf-citation
Cross-Model Attention-Guided Tumor Segmentation for 3D Automated Breast Ultrasound (ABUS) Images
Zhou, Y. F., Chen, H., Li, Y., Cao, X., Wang, S., & Shen, D. G. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3081111 · Cited by 52 · see citing works
Breast ultrasound image segmentation: A coarse‐to‐fine fusion convolutional neural network
Wang, K., Liang, S., Zhong, S., Feng, Q., Ning, Z., & Zhang, Y. (2021) · Medical Physics · doi:10.1002/mp.15006 · Cited by 57 · see citing works
Self-citation
An attention‐supervised full‐resolution residual network for the segmentation of breast ultrasound images
Qu, X., Shi, Y., Hou, Y., & Jiang, J. (2020) · Medical Physics · doi:10.1002/mp.14470 · Cited by 42 · see citing works
“6,7 However, segmentation of BUS images has often presented difficulty because ultrasound images have strong speckle noise, low signal-to-noise ratio, intensity inhomogeneity, and other ultrasound artifacts.”
Background
Imbalanced Loss-Integrated Deep-Learning-Based Ultrasound Image Analysis for Diagnosis of Rotator-Cuff Tear
Lee, K., Kim, J.-Y., Lee, M. H., Choi, C.-H., & Hwang, J. Y. (2021) · Sensors · doi:10.3390/s21062214 · Cited by 42 · see citing works
“Conventional image processing algorithms based on morphological operations such as dilation and erosion have been applied to the diagnosis of locations of RCT, although their performance needs to be further improved [7,8,12].”
Method
Residual Feedback Network for Breast Lesion Segmentation in Ultrasound Image
Wang, K., Liang, S., & Zhang, Y. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87193-2_45 · Cited by 40 · see citing works
Self-citation
Medical Knowledge Constrained Semantic Breast Ultrasound Image Segmentation
Huang, K., Cheng, H.-D., Zhang, Y., Zhang, B., Xing, P., & Ning, C. (2018) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2018.8545272 · Cited by 36 · see citing works
“High-performance automatic segmentation approaches can be divided into semi-automatic and fullyautomatic segmentation approaches [3, 23].”
BackgroundSelf-citation
Swin-Net: A Swin-Transformer-Based Network Combing with Multi-Scale Features for Segmentation of Breast Tumor Ultrasound Images
Zhu, C., Chai, X., Xiao, Y., Liu, X., Zhang, R., Yang, Z., & Wang, Z. (2024) · Diagnostics · doi:10.3390/diagnostics14030269 · Cited by 38 · see citing works
“Traditional image segmentation methods mainly rely on low-level features such as texture and geometric features [5].”
Method
Feature Pyramid Nonlocal Network With Transform Modal Ensemble Learning for Breast Tumor Segmentation in Ultrasound Images
Tang, P., Yang, X., Yang, N., Xiang, S., & Liang, Q. (2021) · IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control · doi:10.1109/tuffc.2021.3098308 · Cited by 37 · see citing works
A Benchmark for Breast Ultrasound Image Segmentation (BUSIS)Preprint
Xian, M., Zhang, Y., Cheng, H.-D., Xu, F., Huang, K., Zhang, B., Ding, J., Ning, C., & Wang, Y. (2018) · arXiv (Cornell University) · Cited by 36 · see citing works
Self-citation
Particle method for segmentation of breast tumors in ultrasound images
Karunanayake, N., Aimmanee, P., Lohitvisate, W., & Makhanov, S. S. (2019) · Mathematics and Computers in Simulation · doi:10.1016/j.matcom.2019.10.009 · Cited by 35 · see citing works
CGRNet: Contour-guided graph reasoning network for ambiguous biomedical image segmentation
Wang, K., Zhang, X., Lü, Y., Zhang, W., & Zhang, W. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.103621 · Cited by 34 · see citing works
Advances in ultrasonography: image formation and quality assessment
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“1 3 beamforming [100–104], ultrasound image processing such as speckle reduction [105, 106], and image segmentation [107, 108], etc.”
A dual-branch and dual attention transformer and CNN hybrid network for ultrasound image segmentation
Zhang, C., Wang, L., Wei, G., Kong, Z., & Qiu, M. (2024) · Frontiers in Physiology · doi:10.3389/fphys.2024.1432987 · Cited by 35 · see citing works
“These merits make it particularly suitable for various clinical needs (Huang et al., 2018), such as thyroid nodule detection (Bi et al., 2023), breast tumor screening (Xian et al., 2018), and gynecologic examinations (Drukker et al., 2020).”
Background
Multiscale superpixel method for segmentation of breast ultrasound
Ilesanmi, A. E., Idowu, O. P., & Makhanov, S. S. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103879 · Cited by 32 · see citing works
A hybrid enhanced attention transformer network for medical ultrasound image segmentation
Jiang, T., Xing, W., Yu, M. Y., & Ta, D. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105329 · Cited by 31 · see citing works
A Review on Region of Interest Segmentation Based on Clustering Techniques for Breast Cancer Ultrasound Images
Muhammad, M., Zeebaree, D. Q., Brifcani, A. M. A., Saeed, J. N., & Zebari, D. A. (2020) · Journal of Applied Science and Technology Trends · doi:10.38094/2020jastt1328 · Cited by 30 · see citing works
Multi-slice low-rank tensor decomposition based multi-atlas segmentation: Application to automatic pathological liver CT segmentation
Shi, C., Xian, M., Zhou, X., Wang, H., & Cheng, H.-D. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102152 · Cited by 29 · see citing works
“The general scheme is also applicable to other medical imaging modalities and organs ( Qin et al. (2019); Xian et al. (2018); Khaleel et al. (2018)) (Section 4.1).”
BackgroundInfluentialSelf-citation
DBL-Net: A dual-branch learning network with information from spatial and frequency domains for tumor segmentation and classification in breast ultrasound image
Zhu, C., Chai, X., Wang, Z., Xiao, Y., Zhang, R., Yang, Z., & Feng, J. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106221 · Cited by 29 · see citing works
Optimal segmentation of image datasets by genetic algorithms using color spaces
Cervantes, J., Cervantes, J., García‐Lamont, F., Yee-Rendón, A., Castilla, J. S. R., & Rodríguez-Mazahua, L. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121950 · Cited by 27 · see citing works
Enhanced Image-Based Endoscopic Pathological Site Classification Using an Ensemble of Deep Learning Models
Nguyen, D. T., Lee, M. B., Pham, T. D., Batchuluun, G., Arsalan, M., & Park, K. R. (2020) · Sensors · doi:10.3390/s20215982 · Cited by 29 · see citing works
“There are several types of cancer such as lung [2,3], breast [4,5], skin [6,7], stomach [8–10], colorectal (also known as colon cancer) [11–16], thyroid [17–19], and brain [20–22] cancers.”
Background
Segmentation and Quantitative Analysis of Photoacoustic Imaging: A Review
Le, T. D., Kwon, S. Y., & Lee, C. (2022) · Photonics · doi:10.3390/photonics9030176 · Cited by 27 · see citing works
“Based on conventional medical imaging modalities [60–62], (CT [63,64], X-ray [65,66], MRI [64,67–69], PET [70–72], and ultrasound [73–75]), segmentation and quantification have been proposed and challenged frequently in response to improvements in imaging techniques.”
Background
HCMNet: A Hybrid CNN-Mamba Network for Breast Ultrasound Segmentation for Consumer Assisted Diagnosis
Xiong, Y., Shu, X., Liu, Q., & Yuan, D. (2025) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2025.3593784 · Cited by 31 · see citing works
Detection of Unlabeled Polystyrene Micro- and Nanoplastics in Mammalian Tissue by Optical Photothermal Infrared Spectroscopy
Duswald, K., Pichler, V., Kopatz, V., Limberger, T., Karl, V., Hennerbichler, D., Zimmerleiter, R., Wadsak, W., Hettich, M., Gruber, E. S., et al. (2025) · Analytical Chemistry · doi:10.1021/acs.analchem.4c05400 · Cited by 25 · see citing works
An Adaptive Region Growing Based on Neutrosophic Set in Ultrasound Domain for Image Segmentation
Jiang, X., Guo, Y., Chen, H., Zhang, Y., & Lu, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2911560 · Cited by 21 · see citing works
“graph-based approaches and learning-based approaches [1].”
BackgroundSelf-citation
Region of Interest Segmentation Based on Clustering Techniques for Breast Cancer Ultrasound Images: A Review
Muhammad, M., Zeebaree, D. Q., Abdulazeez, A. M., Saeed, J. N., & Zebari, D. A. (2020) · Journal of Applied Science and Technology Trends · doi:10.38094/jastt20201328 · Cited by 22 · see citing works
A SURVEY OF ULTRASONOGRAPHY BREAST CANCER IMAGE SEGMENTATION TECHNIQUES
Saeed, J. N. (2020) · Academic Journal of Nawroz University · doi:10.25007/ajnu.v9n1a523 · Cited by 21 · see citing works
“Specifically, techniques including unsupervised and supervised learning have been utilized to perform segmentation of BUS images [59].”
Method
A robust semantic lung segmentation study for CNN-based COVID-19 diagnosis
Aslan, M. F. (2022) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2022.104695 · Cited by 20 · see citing works
“Among these, the medical field that includes applications such as brain tumor segmentation [32], liver segmentation [33], breast image segmentation [34], etc. stands out.”
Effective hybrid attention network based on pseudo-color enhancement in ultrasound image segmentation
Huang, X., Wang, Q., Chen, J., Chen, L., & Chen, Z. (2023) · Image and Vision Computing · doi:10.1016/j.imavis.2023.104742 · Cited by 20 · see citing works
A dual-stage transformer and MLP-based network for breast ultrasound image segmentation
Lin, G., Chen, M., Tan, M., Chen, L., & Chen, J. (2023) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2023.09.001 · Cited by 21 · see citing works
Saliency-guided automatic detection and segmentation of tumor in breast ultrasound images
Ramadan, H., Lachqar, C., & Tairi, H. (2020) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2020.101945 · Cited by 20 · see citing works
A Review of Segmentation Algorithms Applied to B-Mode Breast Ultrasound Images: A Characterization Approach
Kriti, N. A., Virmani, J., & Agarwal, R. (2020) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-020-09469-3 · Cited by 19 · see citing works
“From Table 8, it can also be noted that studies in [248, 249] have been carried out in the past that focus solely on the segmentation of breast tumors from ultrasound images while others describe segmentation as one of the steps among others for diagnosis of breast tumors.”
BackgroundMethod
Supervised and Semi-supervised Methods for Abdominal Organ Segmentation: A Review
Senkyire, I. B., & Liu, Z. (2021) · Machine Intelligence Research · doi:10.1007/s11633-021-1313-0 · Cited by 19 · see citing works
Artificial life for segmentation of fusion ultrasound images of breast abnormalities
Karunanayake, N., Lohitvisate, W., & Makhanov, S. S. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.108838 · Cited by 19 · see citing works
Multimodal image feature fusion for improving medical ultrasound image segmentation
Chen, J., & Chen, J. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105705 · Cited by 19 · see citing works
EU2-Net: A Parameter Efficient Ensemble Model With Attention-Aided Triple Feature Fusion for Tumor Segmentation in Breast Ultrasound Images
Roy, A., Pramanik, P., & Sarkar, R. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3421436 · Cited by 19 · see citing works
“The segmentation of breast ultrasound (BUS) images plays a crucial role in characterizing and localizing breast tumors, representing a critical step in computer-aided diagnosis (CAD) [2].”
Background
Wavelet-based computationally-efficient computer-aided characterization of liver steatosis using conventional B-mode ultrasound images
Amin, M. N., Rushdi, M. A., Marzaban, R. N., Yosry, A., Kim, K., & Mahmoud, A. M. (2019) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2019.03.010 · Cited by 19 · see citing works
“…were lost because of unavailability of accurate and low-cost fatty liver detection techniques and devices; high performance approaches consume much less resources than traditional approaches, and is vitally important to provide an affordable means for early detection of fatty liver disease [57].”
Background
A novel breast ultrasound image automated segmentation algorithm based on seeded region growing integrating gradual equipartition threshold
Fan, H., Meng, F., Liu, Y., Kong, F., Ma, J., & Lv, Z. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-07884-8 · Cited by 18 · see citing works
“It is very difficult to identify threshold from ultrasound images by using histogram [13].”
BackgroundMethod
Syntactic pattern recognition-based diagnostics of fetal palates
Jurek, J., Wójtowicz, W., & Wójtowicz, A. (2020) · Pattern Recognition Letters · doi:10.1016/j.patrec.2020.02.023 · Cited by 17 · see citing works
Classification of Heterogeneity on Multi-Spectral Transmission Image Based on Modulation-Demodulation-Frame Accumulation and Pattern Recognition
Liu, F., Li, G., Yan, W., He, G., Yang, S., & Lin, L. (2019) · IEEE Access · doi:10.1109/access.2019.2930296 · Cited by 16 · see citing works
“Currently, imaging techniques for detecting breast cancer mainly include x-ray [4]–[6], ultrasound [7]–[9], magnetic”
Method

A Benchmark for Breast Ultrasound Image Segmentation (BUSIS)Preprint
Xian, M., Zhang, Y., Cheng, H.-D., Xu, F., Huang, K., Zhang, B., Ding, J., Ning, C., & Wang, Y. (2018) · arXiv (Cornell University) · Cited by 36 · see citing works
Self-citation
BUSIS: A Benchmark for Breast Ultrasound Image SegmentationPreprint
Xian, M., Zhang, Y., Cheng, H., Xu, F., Huang, K., Zhang, B., Ding, J., Ning, C., & Wang, Y. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.03182 · Cited by 5 · see citing works
Self-citation
Methods for the segmentation and classification of breast ultrasound images: a review
Ilesanmi, A. E., Chaumrattanakul, U., & Makhanov, S. S. (2021) · Journal of Ultrasound · doi:10.1007/s40477-020-00557-5 · Cited by 46 · see citing works
Breast ultrasound image segmentation: a survey
Huang, Q., Luo, Y., & Zhang, Q. (2017) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-016-1513-1 · Cited by 222 · see citing works
Automatic superpixel-based segmentation method for breast ultrasound images
Daoud, M. I., Atallah, A. A., Awwad, F. R., Al-Najjar, M., & Alazrai, R. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.11.024 · Cited by 65 · see citing works

Authors: Xian, M., Zhang, Y., Cheng, H. D., Xu, F., Zhang, B., & Ding, J.
Affiliations: University of Idaho (US)
Harbin Institute of Technology (CN)
Utah State University (US)
Corresponding author: Cheng, H.D.
ORCID: Xian, Min
Zhang, Yingtao
Cheng, H.D.
Xu, Fei
Zhang, Boyu
Ding, Jianrui
Venue: Pattern Recognition
Publisher: Elsevier BV
Published: 2018-07
Volume: 79
Pages: 340-355
ISSN: 0031-3203 (ISSN-L)
DOI: 10.1016/j.patcog.2018.02.012
OpenAlex: W2963104294
Cited by: 284
References: 186
Citations by year: 2026: 20 · 2025: 47 · 2024: 41 · 2023: 37 · 2022: 31 · 2021: 42 · 2020: 36 · 2019: 20 · 2018: 10
FWCI: 13.51
Type: journal article
Language: en
Open access: green
arxiv.org
Licence: public-domain
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: Pattern Recognition (publishedVersion)
arXiv (Cornell University) (submittedVersion, public-domain)
arXiv (Cornell University)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
Keywords: breast ultrasound, image segmentation, breast cancer, automatic segmentation, medical image analysis

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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Automatic breast ultrasound image segmentation: a survey

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